Open challenger datasets

import oceanbench

oceanbench.__version__

'0.2.1'

Insert here the code that opens the challenger dataset as challenger_dataset: xarray.Dataset

# SPDX-FileCopyrightText: 2025 Mercator Ocean International <https://www.mercator-ocean.eu/>
#
# SPDX-License-Identifier: EUPL-1.2

from datetime import datetime

import xarray
import dask

# Stage the 35MB-chunk forecasts robustly: read one time-slice at a time, sequentially,
# so to_zarr never bulk-reads the whole variable at once (which hits transient partial-HTTP
# reads -> blosc -1). Pairs with chunks={"time": 1} in the open below.
dask.config.set(scheduler="synchronous")

from oceanbench.core.datetime_utils import generate_dates
from oceanbench.core.interpolate import interpolate_1_degree
from oceanbench.core.remote_http import require_remote_dataset_dimensions, with_remote_http_retries
from oceanbench.core.runtime_configuration import current_runtime_configuration
from oceanbench.core.weekly_stage import maybe_stage_weekly_dataset

_CLOUDFERRO_OCEANBENCH_BASE_URL = "https://s3.waw3-1.cloudferro.com/oceanbench-bucket"
_CLOUDFERRO_ML_FORECASTS_URL = f"{_CLOUDFERRO_OCEANBENCH_BASE_URL}/dev/ml-forecast-outputs"
_LANGYA_LEAD_DAYS_COUNT = 7


def _default_first_day_datetimes() -> list[datetime]:
    return generate_dates("2024-01-03", "2024-12-25", 7)


def _langya_dataset_path(start_datetime: datetime) -> str:
    return f"{_CLOUDFERRO_ML_FORECASTS_URL}/langya/{start_datetime:%Y%m%d}.zarr"


def _prepare_langya_week_dataset(dataset: xarray.Dataset) -> xarray.Dataset:
    week_dataset = require_remote_dataset_dimensions(dataset, ["time"], "langya challenger dataset open")
    week_dataset = week_dataset.isel({"time": slice(0, _LANGYA_LEAD_DAYS_COUNT)})
    week_lead_days_count = week_dataset.sizes["time"]
    return week_dataset.rename({"time": "lead_day_index"}).assign_coords(
        {"lead_day_index": range(week_lead_days_count)}
    )


def _open_langya_week_dataset(first_day_datetime: datetime) -> xarray.Dataset:
    return _prepare_langya_week_dataset(
        xarray.open_dataset(_langya_dataset_path(first_day_datetime), engine="zarr", chunks={"time": 1})
    )


def _remote_langya_dataset(first_day_datetimes: list[datetime]) -> xarray.Dataset:
    return xarray.open_mfdataset(
        list(map(_langya_dataset_path, first_day_datetimes)),
        engine="zarr",
        preprocess=_prepare_langya_week_dataset,
        combine="nested",
        concat_dim="first_day_datetime",
        parallel=False,
    ).assign({"first_day_datetime": first_day_datetimes})


def langya() -> xarray.Dataset:
    first_day_datetimes = _default_first_day_datetimes()

    def open_dataset() -> xarray.Dataset:
        return maybe_stage_weekly_dataset(
            stage_key="challenger",
            dataset_kind="challenger",
            dataset_name="langya",
            first_day_datetimes=first_day_datetimes,
            lead_days_count=_LANGYA_LEAD_DAYS_COUNT,
            open_week_dataset=_open_langya_week_dataset,
            open_remote_dataset=lambda: _remote_langya_dataset(first_day_datetimes),
            attach_source_metadata_when_not_staged=current_runtime_configuration().has_local_stage(),
        )

    return with_remote_http_retries("langya challenger dataset open", open_dataset)


challenger_dataset: xarray.Dataset = interpolate_1_degree(langya())
challenger_dataset

<xarray.Dataset> Size: 11GB
Dimensions:                          (first_day_datetime: 52,
                                      lead_day_index: 7, depth: 32,
                                      latitude: 170, longitude: 360)
Coordinates:
  * depth                            (depth) float32 128B 0.494 1.541 ... 541.1
  * lead_day_index                   (lead_day_index) int64 56B 0 1 2 3 4 5 6
  * first_day_datetime               (first_day_datetime) datetime64[us] 416B ...
  * latitude                         (latitude) float64 1kB -79.5 -78.5 ... 89.5
  * longitude                        (longitude) float64 3kB -179.5 ... 179.5
Data variables:
    sea_water_salinity               (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray>
    sea_water_potential_temperature  (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray>
    eastward_sea_water_velocity      (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray>
    northward_sea_water_velocity     (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray>
    sea_surface_height_above_geoid   (first_day_datetime, lead_day_index, latitude, longitude) float32 89MB dask.array<chunksize=(1, 1, 170, 360), meta=np.ndarray>
Attributes:
    Conventions:                   CF-1.8
    challenger:                    langya
    forecast_reference_time:       2024-01-02
    oceanbench_source_kind:        challenger
    oceanbench_source_name:        langya
    oceanbench_source_resolution:  one_degree

xarray.Dataset

• Dimensions:
  • first_day_datetime: 52
  • lead_day_index: 7
  • depth: 32
  • latitude: 170
  • longitude: 360
• Coordinates: (5)
  • depth
    (depth)
    float32
    0.494 1.541 2.646 ... 453.9 541.1

    axis :

    Z

    long_name :

    Depth

    positive :

    down

    standard_name :

    depth

    units :

    m

    array([4.940250e-01, 1.541375e+00, 2.645669e+00, 3.819495e+00, 5.078224e+00,
           6.440614e+00, 7.929560e+00, 9.572997e+00, 1.140500e+01, 1.346714e+01,
           1.581007e+01, 1.849556e+01, 2.159882e+01, 2.521141e+01, 2.944473e+01,
           3.443415e+01, 4.034405e+01, 4.737369e+01, 5.576429e+01, 6.580727e+01,
           7.785385e+01, 9.232607e+01, 1.097293e+02, 1.306660e+02, 1.558507e+02,
           1.861256e+02, 2.224752e+02, 2.660403e+02, 3.181274e+02, 3.802130e+02,
           4.539377e+02, 5.410889e+02], dtype=float32)

  • lead_day_index
    (lead_day_index)
    int64
    0 1 2 3 4 5 6

    array([0, 1, 2, 3, 4, 5, 6])

  • first_day_datetime
    (first_day_datetime)
    datetime64[us]
    2024-01-03 ... 2024-12-25

    array(['2024-01-03T00:00:00.000000', '2024-01-10T00:00:00.000000',
           '2024-01-17T00:00:00.000000', '2024-01-24T00:00:00.000000',
           '2024-01-31T00:00:00.000000', '2024-02-07T00:00:00.000000',
           '2024-02-14T00:00:00.000000', '2024-02-21T00:00:00.000000',
           '2024-02-28T00:00:00.000000', '2024-03-06T00:00:00.000000',
           '2024-03-13T00:00:00.000000', '2024-03-20T00:00:00.000000',
           '2024-03-27T00:00:00.000000', '2024-04-03T00:00:00.000000',
           '2024-04-10T00:00:00.000000', '2024-04-17T00:00:00.000000',
           '2024-04-24T00:00:00.000000', '2024-05-01T00:00:00.000000',
           '2024-05-08T00:00:00.000000', '2024-05-15T00:00:00.000000',
           '2024-05-22T00:00:00.000000', '2024-05-29T00:00:00.000000',
           '2024-06-05T00:00:00.000000', '2024-06-12T00:00:00.000000',
           '2024-06-19T00:00:00.000000', '2024-06-26T00:00:00.000000',
           '2024-07-03T00:00:00.000000', '2024-07-10T00:00:00.000000',
           '2024-07-17T00:00:00.000000', '2024-07-24T00:00:00.000000',
           '2024-07-31T00:00:00.000000', '2024-08-07T00:00:00.000000',
           '2024-08-14T00:00:00.000000', '2024-08-21T00:00:00.000000',
           '2024-08-28T00:00:00.000000', '2024-09-04T00:00:00.000000',
           '2024-09-11T00:00:00.000000', '2024-09-18T00:00:00.000000',
           '2024-09-25T00:00:00.000000', '2024-10-02T00:00:00.000000',
           '2024-10-09T00:00:00.000000', '2024-10-16T00:00:00.000000',
           '2024-10-23T00:00:00.000000', '2024-10-30T00:00:00.000000',
           '2024-11-06T00:00:00.000000', '2024-11-13T00:00:00.000000',
           '2024-11-20T00:00:00.000000', '2024-11-27T00:00:00.000000',
           '2024-12-04T00:00:00.000000', '2024-12-11T00:00:00.000000',
           '2024-12-18T00:00:00.000000', '2024-12-25T00:00:00.000000'],
          dtype='datetime64[us]')

  • latitude
    (latitude)
    float64
    -79.5 -78.5 -77.5 ... 88.5 89.5

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • longitude
    (longitude)
    float64
    -179.5 -178.5 ... 178.5 179.5

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))

• Data variables: (5)
  • sea_water_salinity
    (first_day_datetime, lead_day_index, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray>

    long_name :

    Salinity

    standard_name :

    sea_water_salinity

    units :

    1e-3

    Array Chunk Bytes 2.66 GiB 239.06 kiB Shape (52, 7, 32, 170, 360) (1, 1, 1, 170, 360) Dask graph 11648 chunks in 175 graph layers Data type float32 numpy.ndarray

  • sea_water_potential_temperature
    (first_day_datetime, lead_day_index, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray>

    long_name :

    Temperature

    standard_name :

    sea_water_potential_temperature

    units :

    degrees_C

    Array Chunk Bytes 2.66 GiB 239.06 kiB Shape (52, 7, 32, 170, 360) (1, 1, 1, 170, 360) Dask graph 11648 chunks in 175 graph layers Data type float32 numpy.ndarray

  • eastward_sea_water_velocity
    (first_day_datetime, lead_day_index, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray>

    long_name :

    Eastward velocity

    standard_name :

    eastward_sea_water_velocity

    units :

    m s-1

    Array Chunk Bytes 2.66 GiB 239.06 kiB Shape (52, 7, 32, 170, 360) (1, 1, 1, 170, 360) Dask graph 11648 chunks in 175 graph layers Data type float32 numpy.ndarray

  • northward_sea_water_velocity
    (first_day_datetime, lead_day_index, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray>

    long_name :

    Northward velocity

    standard_name :

    northward_sea_water_velocity

    units :

    m s-1

    Array Chunk Bytes 2.66 GiB 239.06 kiB Shape (52, 7, 32, 170, 360) (1, 1, 1, 170, 360) Dask graph 11648 chunks in 175 graph layers Data type float32 numpy.ndarray

  • sea_surface_height_above_geoid
    (first_day_datetime, lead_day_index, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 170, 360), meta=np.ndarray>

    long_name :

    Sea surface height

    standard_name :

    sea_surface_height_above_geoid

    units :

    m

    Array Chunk Bytes 84.98 MiB 239.06 kiB Shape (52, 7, 170, 360) (1, 1, 170, 360) Dask graph 364 chunks in 175 graph layers Data type float32 numpy.ndarray

• Indexes: (5)
  • depth
    PandasIndex

    PandasIndex(Index([0.49402499198913574,  1.5413750410079956,  2.6456689834594727,
            3.8194949626922607,   5.078224182128906,   6.440614223480225,
              7.92956018447876,   9.572997093200684,  11.404999732971191,
            13.467140197753906,  15.810070037841797,  18.495559692382812,
            21.598819732666016,  25.211410522460938,  29.444730758666992,
             34.43415069580078,  40.344051361083984,   47.37369155883789,
             55.76428985595703,   65.80726623535156,   77.85385131835938,
              92.3260726928711,  109.72930145263672,  130.66600036621094,
            155.85069274902344,  186.12559509277344,  222.47520446777344,
             266.0403137207031,   318.1274108886719,   380.2130126953125,
             453.9377136230469,   541.0889282226562],
          dtype='float32', name='depth'))

  • lead_day_index
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6], dtype='int64', name='lead_day_index'))

  • first_day_datetime
    PandasIndex

    PandasIndex(DatetimeIndex(['2024-01-03', '2024-01-10', '2024-01-17', '2024-01-24',
                   '2024-01-31', '2024-02-07', '2024-02-14', '2024-02-21',
                   '2024-02-28', '2024-03-06', '2024-03-13', '2024-03-20',
                   '2024-03-27', '2024-04-03', '2024-04-10', '2024-04-17',
                   '2024-04-24', '2024-05-01', '2024-05-08', '2024-05-15',
                   '2024-05-22', '2024-05-29', '2024-06-05', '2024-06-12',
                   '2024-06-19', '2024-06-26', '2024-07-03', '2024-07-10',
                   '2024-07-17', '2024-07-24', '2024-07-31', '2024-08-07',
                   '2024-08-14', '2024-08-21', '2024-08-28', '2024-09-04',
                   '2024-09-11', '2024-09-18', '2024-09-25', '2024-10-02',
                   '2024-10-09', '2024-10-16', '2024-10-23', '2024-10-30',
                   '2024-11-06', '2024-11-13', '2024-11-20', '2024-11-27',
                   '2024-12-04', '2024-12-11', '2024-12-18', '2024-12-25'],
                  dtype='datetime64[us]', name='first_day_datetime', freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([-79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='latitude', length=170))

  • longitude
    PandasIndex

    PandasIndex(Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5, -171.5,
           -170.5,
           ...
            170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,  178.5,
            179.5],
          dtype='float64', name='longitude', length=360))

• Attributes: (6)

  Conventions :

  CF-1.8

  challenger :

  langya

  forecast_reference_time :

  2024-01-02

  oceanbench_source_kind :

  challenger

  oceanbench_source_name :

  langya

  oceanbench_source_resolution :

  one_degree

Evaluation configuration

region = 'global'

Evaluation of challenger dataset using OceanBench

Root Mean Square Deviation (RMSD) of variables compared to GLORYS reanalysis

oceanbench.metrics.rmsd_of_variables_compared_to_glorys_reanalysis(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Sea surface height (m) [sea_surface_height_above_geoid]{surface}	0.073307	0.074883	0.074563	0.075820	0.076922	0.080248	0.082613
Temperature (°C) [sea_water_potential_temperature]{surface}	0.663725	0.646236	0.574785	0.585998	0.597714	0.643391	0.691842
Salinity (PSU) [sea_water_salinity]{surface}	0.824794	0.826847	0.827445	0.830556	0.833757	0.840976	0.846641
Meridional current (m/s) [northward_sea_water_velocity]{surface}	0.124179	0.123809	0.123840	0.123950	0.123692	0.132396	0.136518
Zonal current (m/s) [eastward_sea_water_velocity]{surface}	0.127590	0.128005	0.128644	0.128803	0.128807	0.136684	0.140450
Temperature (°C) [sea_water_potential_temperature]{50m}	0.882116	0.879548	0.853880	0.856498	0.858407	0.896756	0.924946
Salinity (PSU) [sea_water_salinity]{50m}	0.379650	0.380049	0.379631	0.379847	0.380172	0.382141	0.383776
Meridional current (m/s) [northward_sea_water_velocity]{50m}	0.112180	0.111668	0.113353	0.113049	0.112633	0.120097	0.123443
Zonal current (m/s) [eastward_sea_water_velocity]{50m}	0.114761	0.114818	0.116700	0.116487	0.116026	0.122373	0.125502
Temperature (°C) [sea_water_potential_temperature]{100m}	1.024947	1.024766	1.022220	1.026255	1.029320	1.059977	1.080943
Salinity (PSU) [sea_water_salinity]{100m}	0.255442	0.255558	0.255537	0.255779	0.256121	0.257539	0.258432
Meridional current (m/s) [northward_sea_water_velocity]{100m}	0.109073	0.108630	0.110469	0.110109	0.109517	0.115794	0.118546
Zonal current (m/s) [eastward_sea_water_velocity]{100m}	0.114063	0.113928	0.115542	0.115111	0.114606	0.120155	0.122492
Temperature (°C) [sea_water_potential_temperature]{200m}	0.859353	0.859615	0.861704	0.863105	0.864936	0.879726	0.888427
Salinity (PSU) [sea_water_salinity]{200m}	0.160389	0.160451	0.160592	0.160811	0.161154	0.162360	0.163129
Meridional current (m/s) [northward_sea_water_velocity]{200m}	0.106091	0.105959	0.107898	0.107388	0.106625	0.110338	0.111381
Zonal current (m/s) [eastward_sea_water_velocity]{200m}	0.111349	0.111240	0.113120	0.112340	0.111474	0.115152	0.115959
Temperature (°C) [sea_water_potential_temperature]{300m}	0.727596	0.727625	0.729739	0.730865	0.732507	0.745363	0.752374
Salinity (PSU) [sea_water_salinity]{300m}	0.126343	0.126351	0.126520	0.126708	0.127023	0.128144	0.128878
Meridional current (m/s) [northward_sea_water_velocity]{300m}	0.103586	0.103438	0.105325	0.104745	0.103970	0.107291	0.108045
Zonal current (m/s) [eastward_sea_water_velocity]{300m}	0.107435	0.107340	0.109177	0.108414	0.107562	0.110694	0.111261
Temperature (°C) [sea_water_potential_temperature]{500m}	0.561156	0.561623	0.564258	0.565875	0.567847	0.577289	0.583738
Salinity (PSU) [sea_water_salinity]{500m}	0.094660	0.094715	0.094953	0.095218	0.095521	0.096235	0.096861
Meridional current (m/s) [northward_sea_water_velocity]{500m}	0.095126	0.095078	0.096619	0.095999	0.095273	0.098189	0.098760
Zonal current (m/s) [eastward_sea_water_velocity]{500m}	0.098905	0.098890	0.100532	0.099894	0.099216	0.101611	0.101941

Root Mean Square Deviation (RMSD) of Mixed Layer Depth (MLD) compared to GLORYS reanalysis

oceanbench.metrics.rmsd_of_mixed_layer_depth_compared_to_glorys_reanalysis(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Mixed layer depth (m) [ocean_mixed_layer_thickness]{surface}	43.457859	41.852135	38.905724	39.764652	40.528511	42.042381	43.241421

Root Mean Square Deviation (RMSD) of geostrophic currents compared to GLORYS reanalysis

oceanbench.metrics.rmsd_of_geostrophic_currents_compared_to_glorys_reanalysis(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Meridional geostrophic current (m/s) [geostrophic_northward_sea_water_velocity]{surface}	0.062198	0.062952	0.057917	0.059144	0.060579	0.066095	0.071708
Zonal geostrophic current (m/s) [geostrophic_eastward_sea_water_velocity]{surface}	0.061124	0.062961	0.057166	0.057904	0.058859	0.064849	0.070682

Root Mean Square Deviation (RMSD) of variables compared to observations

oceanbench.metrics.rmsd_of_variables_compared_to_observations(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Temperature (°C) [sea_water_potential_temperature]{surface}	0.881401	0.882899	0.778328	0.807849	0.827070	0.895523	0.931393
Temperature (°C) [sea_water_potential_temperature]{0-5m}	0.868259	0.869019	0.816637	0.838810	0.835894	0.899436	0.942262
Temperature (°C) [sea_water_potential_temperature]{5-100m}	0.946703	0.923543	0.890128	0.910004	0.888289	0.947537	0.969433
Temperature (°C) [sea_water_potential_temperature]{100-300m}	0.831636	0.837922	0.821593	0.826408	0.839360	0.846033	0.873484
Temperature (°C) [sea_water_potential_temperature]{300-600m}	0.583342	0.587537	0.574629	0.585648	0.586175	0.605255	0.614357
Salinity (PSU) [sea_water_salinity]{0-5m}	0.251309	0.256866	0.256415	0.299063	0.260086	0.275261	0.267290
Salinity (PSU) [sea_water_salinity]{5-100m}	0.199219	0.199876	0.207717	0.203295	0.204923	0.210001	0.208600
Salinity (PSU) [sea_water_salinity]{100-300m}	0.129182	0.129427	0.129547	0.128864	0.131052	0.130517	0.136803
Salinity (PSU) [sea_water_salinity]{300-600m}	0.085975	0.085753	0.083998	0.086585	0.086647	0.087272	0.090062
Sea level anomaly (m) [sea_surface_height_above_geoid]{surface}	0.113023	0.113939	0.113302	0.113917	0.114453	0.116311	0.116595
Zonal current (m/s) [eastward_sea_water_velocity]{15m}	0.217059	0.216391	0.217603	0.218305	0.215956	0.217347	0.221074
Meridional current (m/s) [northward_sea_water_velocity]{15m}	0.196616	0.195786	0.195721	0.197790	0.197057	0.199094	0.200568

Deviation of Lagrangian trajectories compared to GLORYS reanalysis

oceanbench.metrics.deviation_of_lagrangian_trajectories_compared_to_glorys_reanalysis(
    challenger_dataset,
    region=region,
)

	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6
Lagrangian trajectory deviation (km) []{surface}	10.319209	18.728891	26.091848	32.995644	39.660011

Root Mean Square Deviation (RMSD) of variables compared to GLO12 analysis

oceanbench.metrics.rmsd_of_variables_compared_to_glo12_analysis(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Sea surface height (m) [sea_surface_height_above_geoid]{surface}	0.030669	0.035651	0.035572	0.039854	0.043718	0.051686	0.055626
Temperature (°C) [sea_water_potential_temperature]{surface}	0.426370	0.428766	0.343860	0.385830	0.423189	0.507514	0.570823
Salinity (PSU) [sea_water_salinity]{surface}	0.152245	0.193448	0.221194	0.245684	0.264725	0.285568	0.302687
Meridional current (m/s) [northward_sea_water_velocity]{surface}	0.095836	0.093676	0.083444	0.089713	0.095712	0.116667	0.127044
Zonal current (m/s) [eastward_sea_water_velocity]{surface}	0.095587	0.095930	0.087511	0.093427	0.099639	0.118978	0.129453
Temperature (°C) [sea_water_potential_temperature]{50m}	0.398856	0.443957	0.441710	0.483727	0.524042	0.611721	0.670151
Salinity (PSU) [sea_water_salinity]{50m}	0.071685	0.090335	0.103499	0.115055	0.125060	0.137367	0.146082
Meridional current (m/s) [northward_sea_water_velocity]{50m}	0.075147	0.072734	0.065296	0.071537	0.077566	0.099232	0.109374
Zonal current (m/s) [eastward_sea_water_velocity]{50m}	0.073067	0.073222	0.068867	0.074675	0.080544	0.099703	0.109409
Temperature (°C) [sea_water_potential_temperature]{100m}	0.381682	0.437994	0.459708	0.510768	0.558545	0.654415	0.719436
Salinity (PSU) [sea_water_salinity]{100m}	0.057204	0.072578	0.084766	0.095429	0.104338	0.114638	0.121615
Meridional current (m/s) [northward_sea_water_velocity]{100m}	0.067255	0.065869	0.060056	0.066138	0.072246	0.092587	0.102128
Zonal current (m/s) [eastward_sea_water_velocity]{100m}	0.065489	0.066957	0.064860	0.070753	0.076494	0.093813	0.102431
Temperature (°C) [sea_water_potential_temperature]{200m}	0.271632	0.316281	0.348354	0.394074	0.435205	0.508789	0.553033
Salinity (PSU) [sea_water_salinity]{200m}	0.040958	0.054555	0.065490	0.074893	0.082688	0.091419	0.097602
Meridional current (m/s) [northward_sea_water_velocity]{200m}	0.055928	0.055137	0.050774	0.056619	0.062374	0.079932	0.088284
Zonal current (m/s) [eastward_sea_water_velocity]{200m}	0.053048	0.053847	0.052648	0.058167	0.063533	0.078766	0.086072
Temperature (°C) [sea_water_potential_temperature]{300m}	0.226937	0.267802	0.301104	0.342251	0.378792	0.442942	0.480826
Salinity (PSU) [sea_water_salinity]{300m}	0.034116	0.045844	0.055372	0.063362	0.070020	0.077488	0.082916
Meridional current (m/s) [northward_sea_water_velocity]{300m}	0.051932	0.051497	0.047833	0.053558	0.059153	0.075496	0.083454
Zonal current (m/s) [eastward_sea_water_velocity]{300m}	0.049316	0.050049	0.048799	0.054157	0.059299	0.073518	0.080564
Temperature (°C) [sea_water_potential_temperature]{500m}	0.167218	0.206342	0.239725	0.274775	0.305762	0.353148	0.384042
Salinity (PSU) [sea_water_salinity]{500m}	0.024833	0.034188	0.041942	0.048330	0.053600	0.058773	0.062932
Meridional current (m/s) [northward_sea_water_velocity]{500m}	0.043966	0.045334	0.044247	0.049809	0.055019	0.067981	0.074832
Zonal current (m/s) [eastward_sea_water_velocity]{500m}	0.041487	0.043938	0.044966	0.050222	0.055085	0.066217	0.072150

Root Mean Square Deviation (RMSD) of Mixed Layer Depth (MLD) compared to GLO12 analysis

oceanbench.metrics.rmsd_of_mixed_layer_depth_compared_to_glo12_analysis(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Mixed layer depth (m) [ocean_mixed_layer_thickness]{surface}	32.701984	31.802351	27.397427	29.551638	31.314816	33.931534	36.749249

Root Mean Square Deviation (RMSD) of geostrophic currents compared to GLO12 analysis

oceanbench.metrics.rmsd_of_geostrophic_currents_compared_to_glo12_analysis(
    challenger_dataset,
    region=region,
)

	Lead day 1	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6	Lead day 7
Meridional geostrophic current (m/s) [geostrophic_northward_sea_water_velocity]{surface}	0.043608	0.046217	0.037145	0.040277	0.044083	0.054549	0.063308
Zonal geostrophic current (m/s) [geostrophic_eastward_sea_water_velocity]{surface}	0.046618	0.052690	0.043478	0.045154	0.047931	0.057159	0.065410

Deviation of Lagrangian trajectories compared to GLO12 analysis

oceanbench.metrics.deviation_of_lagrangian_trajectories_compared_to_glo12_analysis(
    challenger_dataset,
    region=region,
)

	Lead day 2	Lead day 3	Lead day 4	Lead day 5	Lead day 6
Lagrangian trajectory deviation (km) []{surface}	7.38767	12.738617	17.038912	21.536791	26.315197